Volume 4, Issue 4, July 2019, Page: 41-47
Big Data-based the Smart Grid Application Analysis
Zhengguang Liu, College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, China
Qiaoyu Liu, College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, China
Mengjiang Wu, Electrical Department, College of Science and Technology of China Three Gorges University, Yichang City, China
Received: Oct. 4, 2019;       Accepted: Oct. 21, 2019;       Published: Oct. 25, 2019
DOI: 10.11648/j.ijimse.20190404.12      View  27      Downloads  5
Abstract
The smart grid is the future development direction of the power industry. The ultimate goal of the smart grid should be to build a real-time monitoring system covering the entire production process of the power system, including power generation, transmission, power transmission, power distribution, and power scheduling. A smart grid is the development trend of the future power industry, and the application analysis of the smart grid is the basis for ensuring economic and safe operation. Smart Grid Application Analysis (SGAA) based on big data, is of considerable significance to the development of the power system. Based on the comprehensive comparison of domestic and foreign literature, this paper puts forward the prediction application of "Big Data +" and makes a simple evaluation of the possible potential power-side load and regulator prediction model of new energy development. It also introduces the shortcomings in the current stage, as well as the critical technologies of the big data industry that need to be developed urgently. The smart grid is the future direction of power industry development, but the current stage of the development of related technology is not enough, this paper gives suggestions for the development of smart grid and big data.
Keywords
Industrial Applications, Big Data, Smart Grids
To cite this article
Zhengguang Liu, Qiaoyu Liu, Mengjiang Wu, Big Data-based the Smart Grid Application Analysis, International Journal of Industrial and Manufacturing Systems Engineering. Vol. 4, No. 4, 2019, pp. 41-47. doi: 10.11648/j.ijimse.20190404.12
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Yu Xin, Lu Jianming, He Pengcheng, Cai Zijian. Research on Smart Grid Load Prediction Algorithms. J. Computer and Digital Engineering, 2019 (09): 2357-2363.
[2]
Ismaila Adeniyi Kamil, Sunday Oyinlola Ogundoyin. A big data anonymous batch verification scheme with conditional privacy preservation for power injection over vehicular network and 5G smart grid slice [J]. Sustainable Energy, Grids and Networks, 2019.
[3]
Ni Zhen, Paul Shuva. A Multistage Game in Smart Grid Security: A Reinforcement Learning Solution. [J]. IEEE transactions on neural networks and learning systems, 2019, 30 (9).
[4]
Anees Junaid, Zhang Hao-Chun, Baig Sobia, Lougou Bachirou Guene. Energy-Efficient Multi-Disjoint Path Opportunistic Node Connection Routing Protocol in Wireless Sensor Networks for Smart Grids. [J]. Sensors (Basel, Switzerland), 2019, 19 (17).
[5]
Saqib Hasan, Abhishek Dubey, Gabor Karsai, Xenofon Koutsoukos. A game-theoretic approach for power systems defense against dynamic cyber-attacks [J]. International Journal of Electrical Power and Energy Systems, 2020, 115.
[6]
Li Tao, Yan Gao. Real-time pricing for smart grid with distributed energy and storage: A noncooperative game method considering spatially and temporally coupled constraints [J]. International Journal of Electrical Power and Energy Systems, 2020, 115.
[7]
Per-Anders Langendahl, Helen Roby, Stephen Potter, Matthew Cook. Smoothing peaks and troughs: Intermediary practices to promote demand side response in smart grids [J]. Energy Research & Social Science, 2019, 58.
[8]
Zhang Yanjun, Yang Xiaodong, Liu Yi, Zheng Dayuan, Bi Shujun. Research on the Frame of Intelligent Inspection Platform Based on Spatio-temporal Data. Computer & Digital Engineering [J], 2019, 47 (03): 616-619+637.
[9]
Ima Essiet, Yanxia Sun, Zenghui Wang. Scavenging differential evolution algorithm for smart grid demand side management [J]. Procedia Manufacturing, 2019, 35.
[10]
Energy; Studies from Hong Kong Polytechnic University Update Current Data on Energy (Game Theory Based Interactive Demand Side Management Responding To Dynamic Pricing In Price-based Demand Response of Smart Grids) [J]. Energy Weekly News, 2019.
[11]
Francesco D’ Ettorre, Mattia De Rosa, Paolo Conti, Daniele Testi, Donal Finn. Mapping the energy flexibility potential of single buildings equipped with optimally-controlled heat pump, gas boilers and thermal storage [J]. Sustainable Cities and Society, 2019, 50.
[12]
Yi Liu, Jiawen Peng, and Zhihao Yu. 2018. Big Data Platform Architecture under The Background of Financial Technology: In The Insurance Industry As An Example. In Proceedings of the 2018 International Conference on Big Data Engineering and Technology (BDET 2018). ACM, New York, NY, USA, 31-35.
[13]
Mohammad Taheri Tehrani, Ali Mohammad Afshin Hemmatyar. Welfare-aware strategic demand control in an intelligent market-based framework: Move towards sustainable smart grid [J]. Applied Energy, 2019, 251.
[14]
L. Yi and W. Yi, "Decision Tree Model in the Diagnosis of Breast Cancer," 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC), Dalian, China, 2017, pp. 176-179. doi: 10.1109/ICCTEC.2017.00046.
[15]
Hamed Hashemi-Dezaki, Ali-Mohammad Hariri, Maryam A. Hejazi. Impacts of load modeling on generalized analytical reliability assessment of smart grid under various penetration levels of wind/solar/non-renewable distributed generations [J]. Sustainable Energy, Grids and Networks, 2019, 20.
[16]
Amevi Acakpovi, Ruhiya Abubakar, Nana Yaw Asabere, Issah B. Majeed. Barriers and Prospects of Smart Grid Adoption in Ghana [J]. Procedia Manufacturing, 2019, 35.
[17]
Wu Weihong, Han Yu, Feng Lin, Li Guojie, Jiang Xiuchen. “AI+” based smart grids prediction analysis Journal of Shanghai Jiaotong University, 2018, 52 (10): 1206-1219.1266.
[18]
Zhang Dongxia, Miao Xin, Liu Liping, Zhang Yan, Liu Scientific Research. Smart Grid Big Data Technology Development Research. J, China Motor Engineering News, 2015, 35 (01): 2-12.
[19]
Song Yaqi, Zhou Guoliang, Zhu Yongli. Present Status and Challenges of Big Data Processing in Smart Grid. Grid Technology, 2013, 37 (04): 927-935.
[20]
Hu Xuehao. Smart grid: a development trend of future power grid [J]. Power System Technology, 2009, 33 (14): 1-5 (in Chinese).
[21]
Zhou Hui, Niu Wenjie, Wang Yi. Analysis of clients’ credit based on theirs paying behaviors [J]. Power Demand-Side Management, 2006, 8 (6): 12-16 (in Chinese).
[22]
Zhang Guangbin, Shu Hongchun, Yu Jilai. Travelling wave field data contingency screening based on semi-supervised clustering using generalized current modal component [J]. Proceedings of the CSEE, 2012, 32 (10): 150-158 (in Chinese).
[23]
Xi Fang, Satyajayant Misra, Guoliang Xue, et al. Smart Grid, the new and improved power grid: a survey [J]. IEEE Communications Surveys and Tutorials (COMST), 2012, 14 (4): 944-980.
[24]
Z. Zhao, J. Wang and Y. Liu, "User Electricity Behavior Analysis Based on K-Means Plus Clustering Algorithm," 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC), Dalian, China, 2017, pp. 484-487. doi: 10.1109/ICCTEC.2017.00111.
Browse journals by subject